{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.74   2.202  4.497  1.151  1.649 13.43  12.191  6.049  5.299  0.899]\n",
      "[ 1.          3.11111111  5.22222222  7.33333333  9.44444444 11.55555556\n",
      " 13.66666667 15.77777778 17.88888889 20.        ]\n"
     ]
    }
   ],
   "source": [
    "t = - np.array([\n",
    "    1.108 - 2.848,\n",
    "    0.697 - 2.899,\n",
    "    6.847 - 11.344,\n",
    "    0.897 - 2.048,\n",
    "    1.232 - 2.881,\n",
    "    1.570 - 15,\n",
    "    1.156 - 13.347,\n",
    "    11.010 - 17.059,\n",
    "    9.251 - 14.550,\n",
    "    # 3.247 - 4.994,\n",
    "    3.997 - 4.896\n",
    "])\n",
    "print(t)\n",
    "p = np.linspace(1, 20, 10)\n",
    "print(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.43  12.191  6.049  5.299  4.497  2.202  1.74   1.649  1.151  0.899]\n",
      "[ 1.          3.11111111  5.22222222  7.33333333  9.44444444 11.55555556\n",
      " 13.66666667 15.77777778 17.88888889 20.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 520x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.sort(t)\n",
    "t = t[::-1]\n",
    "print(t)\n",
    "print(p)\n",
    "\n",
    "fig = plt.figure(figsize=(5.2, 4))\n",
    "ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "ax1.scatter(p, t)\n",
    "plt.title(\"t - n curve\")\n",
    "plt.xlabel(\"n / s⁻¹\")\n",
    "plt.ylabel(\"t / s\")\n",
    "plt.legend(['data'])\n",
    "ax1.grid()\n",
    "\n",
    "l = 22.06\n",
    "v = l / t\n",
    "ax2 = fig.add_axes([1.1, 0.1, 0.8, 0.8])\n",
    "ax2.scatter(p, v)\n",
    "plt.title(\"v - n curve\")\n",
    "plt.xlabel(\"n / s⁻¹\")\n",
    "plt.ylabel(\"v / mm∙s⁻¹\")\n",
    "plt.legend(['data'])\n",
    "ax2.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
